Medical information management apparatus, data structure of medical information, and storage medium

ABSTRACT

A medical information management apparatus includes a hardware processor that manages first dynamic information obtained by performing dynamic radiographing on a first subject that does not have a disease and first attribute information of the first subject. The first dynamic information and the first attribution information is associated with each other.

CROSS-REFERENCE TO RELATED APPLICATIONS

The entire disclosure of Japanese Patent Application No. 2021-005831filed on Jan. 18, 2021 is incorporated herein by reference in itsentirety.

BACKGROUND Technological Field

The present invention relates to a medical information managementapparatus, a data structure of medical information, and a storage mediumstoring a medical information management program.

Description of the Related Art

Typically, a diagnosis method for detecting an abnormality such as alesion by comparing a current image with an image obtained byradiographing a patient in the past is used. However, an image of anormal case (hereinafter, referred to as a normal image) in the past fora diagnosis target patient does not always exist, and thus, a techniqueof extracting an image having a normal structure similar to a normalstructure of the diagnosis target patient from a large-scale databaseconstituted with normal images and using a similarity difference imagewhich indicates a difference between the extracted normal image and animage of the diagnosis target patient (hereinafter, referred to as adiagnosis target image) as a diagnosis assistance image has beenproposed (see JP 2016-174735A). However, a shape of an organ variesbetween individuals, which causes artifact in the similarity differenceimage, and thus, the similarity difference image is generated in a casewhere a shape of an organ in the normal image within the image databasematches a shape of an organ in the diagnosis target image.

By the way, the above-described related art relates to a radiographicstill image, and use of an image obtained by radiographing at a maximalexpiratory level is known as an example of a normal image of a stillimage. Meanwhile, while a diagnosis using a dynamic image (dynamicinformation) obtained through dynamic radiographing is tried in recentyears, what kind of dynamic image is a normal image is not sufficientlycomprehended, and thus, a dynamic image is also determined on the basisof knowledge about a normal image of a still image.

SUMMARY

However, there is a case where a large difference cannot be found in ashape of a structure such as an organ and a bone in an image at amaximal expiratory level between a normal image and an image of apatient having a disease, and there is a problem that a doctor cannotsufficiently make a diagnosis using a dynamic image only with knowledgeabout a normal image of a still image.

Further, an information amount of a dynamic image is significantlylarger than an information amount of a still image, and thus, importanceof a normal image which becomes a diagnosis criterion increases.

Further, a dynamic image, which is a moving image, requires more timefor viewing than a still image. Viewing of all frames of a moving imageleads to rapid increase in man-hours of a doctor, which is unacceptable.It is therefore necessary to achieve viewing efficiency and diagnosticefficiency of a doctor in a dynamic state compared to a still image.This requires diagnosis support such as analysis of moving image data,marking of candidates for abnormal parts and informing by highlighting,or the like. Extraction, or the like, of candidates for abnormal partsfor the purpose of such diagnosis support requires a normal image, andthus, collection of normal images becomes more important in a movingimage than in a still image.

Further, if it is erroneously determined that there is an abnormalityalthough a diagnosis target image does not include a disease,re-radiographing of a patient continues, which considerably increasesradiation exposure compared to re-radiographing of a still image. Adiagnosis using a normal image is effective also to prevent such uselessradiation.

The present invention has been made in view of the problems in relatedart described above, and objects of the present invention are to enabledynamic information obtained by performing dynamic radiographing on asubject who does not have a disease to be utilized later.

To achieve at least one of the abovementioned objects, according to anaspect of the present invention, there is provided a medical informationmanagement apparatus including a hardware processor that manages firstdynamic information obtained by performing dynamic radiographing on afirst subject that does not have a disease and first attributeinformation of the first subject, the first dynamic information and thefirst attribution information being associated with each other.

To achieve at least one of the abovementioned objects, according toanother aspect of the present invention, there is provided a datastructure of medical information to be used by a dynamic informationprocessing apparatus that processes dynamic information obtained byperforming dynamic radiographing, the data structure including: dynamicinformation data obtained by performing dynamic radiographing on asubject that does not have a disease; and attribute information data ofthe subject associated with the dynamic information data.

To achieve at least one of the abovementioned objects, according toanother aspect of the present invention, there is provided anon-transitory computer-readable storage medium storing a medicalinformation management program causing a computer to perform amanagement process of managing first dynamic information obtained byperforming dynamic radiographing on a first subject that does not have adisease and first attribute information of the first subject, the firstdynamic information and the first attribution information beingassociated with each other.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages and features provided by one or more embodiments of theinvention will become more fully understood from the detaileddescription given hereinbelow and the appended drawings which are givenby way of illustration only, and thus are not intended as a definitionof the limits of the present invention, wherein:

FIG. 1 is a view illustrating an entire configuration of a medicalinformation management system according to a first embodiment of thepresent invention;

FIG. 2 is a block diagram illustrating a functional configuration of amedical information management apparatus;

FIG. 3 is a view illustrating an example of a data configuration of acase database;

FIG. 4 is a flowchart illustrating case data registration processing;

FIG. 5 is a flowchart illustrating examination mode processing;

FIG. 6 is an example of a search result screen;

FIG. 7 is a flowchart illustrating comparison mode processing;

FIG. 8 is an example of an analysis result screen;

FIG. 9 is an example of an analysis result screen;

FIG. 10 is an example of machine learning data to be utilized in asecond embodiment of the present invention;

FIG. 11 is a conceptual diagram illustrating machine learning processingusing patient parameters and diagnosis results;

FIG. 12 is a conceptual diagram illustrating inference processing ofpredicting diagnosis prediction results from patient parameters using adiscriminator which has learned;

FIG. 13 is a view illustrating processing of creating a normal modelthrough machine learning utilizing patient parameters of normal casedata in a third embodiment of the present invention; and

FIG. 14 is a view illustrating processing of extracting characteristicson which importance is placed in determination as to whether or notthere is an abnormality, through machine learning utilizing patientparameters of case data in a fourth embodiment of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Hereinafter, embodiments of a medical information management apparatus,a data structure of medical information, and a medical informationmanagement program according to the present invention will be describedwith reference to the drawings. However, the scope of the presentinvention is not limited to the illustrated examples.

First Embodiment (Configuration of Medical Information ManagementSystem)

FIG. 1 illustrates an entire configuration of a medical informationmanagement system 100 in a first embodiment of the present invention.

As illustrated in FIG. 1, the medical information management system 100includes a medical information management apparatus 10, a radiographingconsole 20, a radiographing apparatus 30, a test apparatus 40, anelectronic health record apparatus 50, and a medical image managementsystem 90. The medical information management apparatus 10, theradiographing console 20, the test apparatus 40, the electronic healthrecord apparatus 50, and the medical image management system 90 areconnected via a communication network N such as a local area network(LAN).

The medical information management apparatus 10 includes a case database152 and manages dynamic information such as a dynamic image obtained byradiographing by the radiographing apparatus 30 and attributeinformation of a subject who is a radiographing target in associationwith each other.

The radiographing console 20 outputs a radiation irradiation conditionand an image read condition to the radiographing apparatus 30 to controlradiographing by the radiographing apparatus 30 and read operation of aradiograph.

The radiographing apparatus 30 is an apparatus which can radiograph adynamic state such as shape change including expansion and contractionof a lung associated with respiratory movement and heartbeats. Indynamic radiographing, a plurality of images indicating a dynamic stateof a subject are acquired by repeatedly irradiating the subject withpulsed radiation such as an X ray at predetermined time intervals (pulseradiation) or continuously irradiating the subject at a low dose in aseamless manner (continuous radiation). A series of images obtainedthrough dynamic radiographing will be referred to as a dynamic image.Further, each of a plurality of images which constitute a dynamic imagewill be referred to as a frame image. A case will be described belowusing an example where dynamic radiographing is performed through pulseradiation.

Dynamic radiographing includes radiographing of a moving image, but doesnot include radiographing of a still image while displaying a movingimage. Further, a dynamic image includes a moving image, but does notinclude an image obtained through radiographing of a still image whiledisplaying a moving image.

The radiographing apparatus 30 includes a radiation source 31, aradiation irradiation control apparatus 32, a radiation detector 33 anda read control apparatus 34.

The radiation source 31 is disposed at a position facing the radiationdetector 33 across the subject and irradiates the subject with radiationin accordance with control of the radiation irradiation controlapparatus 32.

The radiation irradiation control apparatus 32 is connected to theradiographing console 20 and controls the radiation source 31 on thebasis of the radiation irradiation condition input from theradiographing console 20 to perform radiographing. The radiationirradiation condition includes, for example, a pulse rate, a pulsewidth, a pulse interval, the number of radiographing frames per oneradiographing, a tube current, a tube voltage, a type of an addedfilter, and the like. The pulse rate is the number of times of radiationirradiation per one second. The pulse width is a radiation irradiationperiod per one radiation irradiation. The pulse interval is a periodfrom when one radiation irradiation is started until the next radiationirradiation is started.

The radiation detector 33 is constituted with a semiconductor imagesensor such as a flat panel detector (FPD). The FPD includes a glasssubstrate, or the like, and a plurality of detection elements (pixels)which detect at least radiation which is radiated from the radiationsource 31 and penetrates the subject, in accordance with intensity ofthe radiation, convert the detected radiation into electrical signalsand accumulate the electrical signals are arranged in a matrix atpredetermined positions on the substrate. Each pixel includes a switchsuch as a thin film transistor (TFT).

The read control apparatus 34, which is connected to the radiographingconsole 20, controls the switches of the respective pixels of theradiation detector 33 on the basis of an image read condition input fromthe radiographing console 20 to switch read of the electrical signalsaccumulated at the respective pixels and acquires image data by readingelectrical signals accumulated at the radiation detector 33. This imagedata is each frame image of a dynamic image. The read control apparatus34 outputs the acquired dynamic image to the radiographing console 20.The image read condition includes, for example, a frame rate, a frameinterval, a pixel size, an image size (matrix size), and the like. Theframe rate is the number of frame images acquired per one second andmatches the pulse rate. The frame interval, which is a period from whenoperation of acquiring one frame image is started until when operationof acquiring the next frame image is started, matches the pulseinterval.

The test apparatus 40 is an apparatus that performs a pulmonary functiontest (PFT) on a patient. In the pulmonary function test, vital capacity(VC), total lung capacity (TLC), functional residual capacity (FRC), aresidual volume (RV), RV/TLC, an expiratory reserve volume (ERV), aforced expiratory volume in one second (FEV1), or the like, aremeasured.

The electronic health record apparatus 50 manages health recordinformation on each patient. The health record information includespatient information regarding a patient.

The medical image management system 90 manages image information on eachpatient. The medical image management system 90 includes imageinformation regarding a patient.

(Configuration of Medical Information Management Apparatus)

FIG. 2 illustrates a functional configuration of the medical informationmanagement apparatus 10. As illustrated in FIG. 2, the medicalinformation management apparatus 10 includes a controller 11, anoperator 12, a display 13, a communicator 14 and a storage 15, which areconnected with a bus.

The controller 11 includes a central processing unit (CPU), a randomaccess memory (RAM), or the like. The CPU of the controller 11 reads outa system program and various kinds of processing programs stored in thestorage 15, loads the programs to the RAM and executes various kinds ofprocessing in accordance with the loaded programs.

The operator 12, which includes a keyboard including cursor keys,character and number entry keys, various kinds of function keys, and thelike, and a pointing device such as a mouse, outputs an instructionsignal input through key operation and mouse operation on the keyboardto the controller 11. Further, the operator 12 may include a touch panelon a display screen of the display 13, in which case the operator 12outputs an instruction signal input via the touch panel to thecontroller 11.

The display 13, which includes a monitor such as a liquid crystaldisplay (LCD), displays various kinds of screens in accordance with aninstruction of a display signal input from the controller 11.

The communicator 14, which includes a LAN adapter, a modem, a terminaladapter (TA), or the like, controls data transmission/reception witheach apparatus connected to the communication network N.

The storage 15 includes a non-volatile semiconductor memory, a harddisk, or the like. The storage 15 stores data such as various kinds ofprograms including the medical information management program 151 to beexecuted at the controller 11, parameters required for executingprocessing by the programs and processing results. Further, the storage15 stores the case database 152.

FIG. 3 illustrates an example of a data configuration of the casedatabase 152.

As illustrated in FIG. 3, dynamic information, attribute information anddiagnosis results are stored in the case database 152 in associationwith one another. In the case database 152, data corresponding to onerow is dealt with as one piece of case data.

The dynamic information, which is information obtained by performingdynamic radiographing on the subject, includes at least one of a dynamicimage or motion information.

The dynamic image is a series of images (image data) obtained throughdynamic radiographing. In the case database 152, path name indicating alocation where a file of the dynamic image is stored may be associatedin place of the dynamic image.

The motion information is information representing motion of parts (forexample, parts relating to a respiratory organ, a circulatory organ,orthopedics and swallowing) of the subject obtained from the dynamicimage. The motion information includes a position obtained for eachframe image, speed obtained from a difference between frame images, andinformation such as maximum speed and a change rate of a size which canbe analytically obtained from these kinds of information. As the motioninformation, for example, information obtained by quantifying motion ofparts such as speed of a diaphragm, a change rate of an area of a lungfield and a restenosis rate of a diameter of an airway is used. Further,in a case where the motion information is time-series data, theinformation may be graphically indicated over time.

In a case where a posterior costal bone, a breast bone, a collar bone, aspine, a diaphragm and a rib cage are made radiographing targetportions, as the motion information, time-series change of positions,time-series change of speed, maximum distances from initial positions,maximum/minimum speed, or the like, are used.

In a case where a heart is made a radiographing target portion, as themotion information, time-series change of a size, time-series change ofconcentration of signal values, a size change rate, a change rate ofconcentration of signal values, or the like, are used.

In a case where an aortic arch is made a radiographing target portion,as the motion information, time-series change of concentration of signalvalues, a change rate of concentration of signal values, or the like,are used.

In a case where a trachea is made a radiographing target portion, as themotion information, time-series change of a size of tracheal dimension,a degree of narrowing of the tracheal dimension, or the like, are used.

In a case where a lung field is made a radiographing target portion, asthe motion information, time-series change of a size of the lung field,a change rate of an area of a maximum/minimum lung field, a change rateof concentration of signal values, or the like, are used.

The attribute information is information indicating attributes of thesubject who is made a radiographing target of a dynamic image. Examplesof the attribute information can include, for example, age, sex, bodyheight, body weight, BMI, race and smoking history. The attributeinformation does not include information which specifies an individual.Examples of the information which specifies an individual can includename, address and phone number.

Further, test results such as results of a pulmonary function test maybe used as the attribute information. The results of the pulmonaryfunction test include vital capacity (VC), total lung capacity (TLC),functional residual capacity (FRC), a residual volume (RV), RV/TLC, anexpiratory reserve volume (ERV), a forced expiratory volume in onesecond (FEV1), or the like.

Further, radiographing conditions (such as the radiation irradiationcondition and the image read condition) upon radiographing of a dynamicimage at the radiographing apparatus 30 may be used as the attributeinformation. The radiographing conditions may include date and time ofradiographing, a portion, a radiographing direction, or the like.

The diagnosis results, which are diagnosis results for dynamicinformation (the subject who is made a radiographing target of thedynamic image), include a normal/abnormal flag and diagnosis name.

The normal/abnormal flag is a flag indicating whether or not the subjecthas a disease, and in a case where the subject does not have a disease,the flag indicates “normal”, while in a case where the subject has adisease, the flag indicates “abnormal”.

The diagnosis name is diagnosis name (such as name of a disease) in acase where the normal/abnormal flag is “abnormal”, that is, in a casewhere the subject has a disease. In a case where the normal/abnormalflag is “normal”, the diagnosis name is “no disease”. Note that in acase where the normal/abnormal flag is “normal”, the diagnosis name maybe made blank.

A disease includes, for example, diseases relating to a respiratoryorgan, a circulatory organ, orthopedics and swallowing. Morespecifically, a disease relating to a respiratory organ includes achronic obstructive pulmonary disease (COPD) and pneumonia, a diseaserelating to a circulatory organ includes a heart failure and pulmonaryembolism, and a disease relating to orthopedics includes arthropathy andfracture.

Further, as the normal/abnormal flag, a normal/abnormal flag for aparticular disease such as, for example, a normal/abnormal flag for adisease relating to a respiratory organ and a normal/abnormal flag for acirculatory organ may be added as well as a normal/abnormal flag for alldiseases.

In the case database 152, a record for which the normal/abnormal flag is“normal” is a normal case, and a record for which the normal/abnormalflag is “abnormal” is an abnormal case.

The case database 152 has a data structure of medical information whichis to be used at the medial information management apparatus 10 as adynamic information processing apparatus that processes dynamicinformation obtained through dynamic radiographing. Here, the dynamicinformation to be processed is a dynamic image or motion information ofthe diagnosis target patient. The processing to be performed on thedynamic information includes display processing (such as simultaneousdisplay with normal case data), statistical analysis, inference (machinelearning, deep learning) as well as image processing on the dynamicimage of the diagnosis target patient.

Statistical analysis, which is processing of performing statisticalanalysis on data to be processed, includes processing of calculating anaverage value, a median value, standard deviation, or the like,processing of analyzing distribution of data by creating histogram, orthe like.

Inference is processing of deriving inference results from the data tobe processed on the basis of results obtained through machine learning.Use of case data managed in the case database 152 in machine learningwill be described in a second embodiment and the subsequent embodiments.

A user can utilize the case data managed in the case database 152 byaccessing the case database 152. For example, in a case where it isdesired to analyze motion information of a diaphragm (such as a positionand speed of the diaphragm) while specifying age and sex, the userextracts case data from the case database 152 while specifying an agerange and sex as search conditions. Then, the user calculates the numberof pieces of data (the number of extracted cases), average age (anaverage value of age of the extracted case data), an average amplitude(an average value of amplitudes of the diaphragm in the extracted casedata), average speed (an average value of speed of the diaphragm in theextracted case data), displays analysis results, stores the analysisresults in a recording medium, or transmits the analysis results to anexternal apparatus.

The controller 11 manages dynamic information (hereinafter, referred toas “normal dynamic information”) obtained by performing dynamicradiographing on a subject who does not have a disease (for example, ina case where the dynamic information is obtained through dynamicradiographing of parts relating to a respiratory organ, the subject mayhave a disease (for example, fracture) other than the disease of therespiratory organ (such as, for example, an arm which relates toorthopedics)) and attribute information of the subject who does not havea disease (hereinafter, referred to as “normal attribute information”)in association with each other. In other words, the controller 11functions as a manager (hardware processor).

The normal dynamic information includes both a case where radiographingis performed while it is understood in advance that the subject does nothave a disease and a case where it is diagnosed that the subject doesnot have a disease on the basis of the dynamic information afterradiographing.

The controller 11 (manager) further manages third dynamic information(hereinafter, referred to as “abnormal dynamic information”) obtained byperforming dynamic radiographing on a third subject who has a diseaseand attribute information of the third subject who has a disease(hereinafter, referred to as “abnormal attribute information”) inassociation with each other.

The abnormal dynamic information includes both a case whereradiographing is performed while it is understood in advance that thesubject has a disease and a case where it is diagnosed that the subjecthas a disease on the basis of the dynamic information afterradiographing.

The controller 11 determines normality or abnormality of second dynamicinformation (hereinafter, referred to as “diagnosis target dynamicinformation”) obtained by performing dynamic radiographing on a secondsubject (hereinafter, referred to as a “diagnosis target patient”) onthe basis of the normal dynamic information. In other words, thecontroller 11 functions as a determiner (hardware processor).

The normality, which is information indicating whether or not thediagnosis target dynamic information is normal, includes a degree ofnormality of the diagnosis target dynamic information.

The abnormality, which is information indicating whether or not thediagnosis target dynamic information is abnormal, includes a degree ofabnormality of the diagnosis target dynamic information.

For example, the controller 11 compares the normal dynamic informationwith the diagnosis target dynamic information, and in a case where thediagnosis target dynamic information is similar to (not different from)the normal dynamic information, determines that the diagnosis targetdynamic information is normal. Meanwhile, in a case where the diagnosistarget dynamic information is largely different from the normal dynamicinformation, the controller 11 determines that the diagnosis targetdynamic information is abnormal. Note that a predetermined threshold, orthe like, can be used in determination as to whether the diagnosistarget dynamic information is similar to or different from the normaldynamic information.

The controller 11 (determiner) determines normality or abnormality ofthe diagnosis target dynamic information on the basis of the normaldynamic information and the normal attribute information. Specifically,the controller 11 determines normality or abnormality of the diagnosistarget dynamic information by comparing normal dynamic informationcorresponding to attribute information (normal attribute information)which is the same as or similar to attribute information of thediagnosis target patient with the diagnosis target dynamic information.A predetermined threshold, or the like, can be used to determinesimilarity in the attribute information.

The controller 11 (determiner) determines normality or abnormality ofthe diagnosis target dynamic information by performing dynamicradiographing on the diagnosis target patient on the basis of the normaldynamic information and the abnormal dynamic information.

For example, the controller 11 compares the normal dynamic informationand the abnormal dynamic information with the diagnosis target dynamicinformation and, in a case where the diagnosis target dynamicinformation is more similar to the normal dynamic information than theabnormal dynamic information, the controller 11 determines that thediagnosis target dynamic information is normal. Meanwhile, in a casewhere the diagnosis target dynamic information is more similar to theabnormal dynamic information than the normal dynamic information, or ina case where the diagnosis target dynamic information is largelydifferent from the normal dynamic information, the controller 11determines that the diagnosis target dynamic information is abnormal.Note that a predetermined threshold, or the like, can be used todetermine whether the diagnosis target dynamic information is similar toor different from the normal dynamic information or the abnormal dynamicinformation.

The controller 11 (determiner) determines normality or abnormality ofthe diagnosis target dynamic information on the basis of the normaldynamic information, the normal attribute information, the abnormaldynamic information and the abnormal attribute information.Specifically, the controller 11 determines normality or abnormality ofthe diagnosis target dynamic information by comparing normal dynamicinformation and abnormal dynamic information corresponding to attributeinformation (normal attribute information, abnormal attributeinformation) which is the same as or similar to the attributeinformation of the diagnosis target patient, with the diagnosis targetdynamic information.

The controller 11 (determiner) determines normality or abnormality ofthe diagnosis target dynamic information through statistical analysis.Distribution (such as histogram), an average value, a median value,standard deviation, or the like, of the motion information can beutilized in statistical analysis.

The controller 11 outputs statistical data of the normal dynamicinformation, statistical data of the abnormal dynamic information, anddiagnosis target dynamic information obtained by performing dynamicradiographing on the diagnosis target patient. In other words, thecontroller 11 functions as outputter.

The controller 11 (outputter) outputs the statistical data of the normaldynamic information, the statistical data of the abnormal dynamicinformation and the diagnosis target dynamic information on the samescreen.

(Operation of Medical Information Management Apparatus)

Operation at the medical information management apparatus 10 will bedescribed next.

FIG. 4 is a flowchart illustrating case data registration processing tobe executed by the medical information management apparatus 10. The casedata registration processing, which is processing of collecting andregistering data of normal cases and abnormal cases, is implementedthrough software processing by coordination of the CPU of the controller11 and the medical information management program 151 stored in thestorage 15.

First, if dynamic radiographing is performed on the subject at theradiographing apparatus 30, the controller 11 of the medical informationmanagement apparatus 10 acquires a dynamic image from the radiographingconsole 20 via the communicator 14 (step S1). The dynamic image includesa plurality of frame images (such as, for example, 15 frames persecond).

Then, the controller 11 acquires attribute information corresponding tothe subject who is made a radiographing target in the dynamic image(step S2). Specifically, the controller 11 causes an input screen of theattribute information to be displayed at the display 13 and acceptsinput of the attribute information through operation by the user fromthe operator 12.

Note that the controller 11 may acquire attribute information fromadditional information added to a file of the dynamic image or mayacquire patient information (attribute information) corresponding to thesubject from the electronic health record apparatus 50 via thecommunicator 14. Here, in a case where the acquired information includesinformation which specifies an individual, such as name of the patient,the controller 11 excludes this information.

Further, the controller 11 may acquire a result of a pulmonary functiontest performed on the subject (patient) from the test apparatus 40 viathe communicator 14 as the attribute information corresponding to thesubject.

Then, the controller 11 analyzes the dynamic image acquired in step S1and calculates motion information (step S3). The motion informationincludes motion information relating to a respiratory organ, acirculatory organ, orthopedics and swallowing. For example, the motioninformation relating to a respiratory organ includes speed of adiaphragm, a change rate of an area of a lung field, a restenosis rateof a diameter of an airway, or the like. Further, the motion informationrelating to a circulatory organ includes speed of motion of a cardiacwall. Further, the motion information relating to orthopedics includestrajectory (information on change of positions) of bending andstretching of a joint such as knee and elbow and stretching speed of thejoint. For example, the controller 11 detects a position of thediaphragm from a dynamic image (a plurality of frame images) obtained byradiographing a front chest and calculates speed of the diaphragmbetween frame images. Further, the controller 11 obtains maximum speedof the diaphragm from motion of the diaphragm in a series of dynamicimages.

Further, the controller 11 detects a position (region) of a lung fieldfrom the dynamic image obtained by radiographing the front chest andcalculates an area of the lung field for each frame image. Thecontroller 11 then calculates a change rate of the area of the lungfield from motion of the lung field in a series of dynamic images.

Further, the controller 11 detects a position of an airway from adynamic image obtained by radiographing the front chest and calculates adiameter of the airway for each frame image. The controller 11 thencalculates a restenosis rate of the diameter of the airway from motionof the airway in a series of dynamic images.

Then, the controller 11 determines whether or not the dynamic imageacquired in step S1 includes a disease (step S4). Specifically, thecontroller 11 causes an input screen of diagnosis results to bedisplayed at the display 13 and accepts input of whether or not thedynamic image includes a disease (normal/abnormal) and diagnosis name(in a case where the dynamic image includes a disease) through operationby the user from the operator 12.

In a case where the dynamic image does not include a disease (step S4:NO), the controller 11 stores the dynamic information (the dynamicimage, the motion information) and the attribute information in the casedatabase 152 in association with each other (step S5) and sets diagnosisname corresponding to this case as “no disease”. Further, the controller11 sets the normal/abnormal flag corresponding to this case as “normal”(step S6).

In step S4, in a case where the dynamic image includes a disease (stepS4: YES), the controller 11 stores the dynamic information (the dynamicimage, the motion information), the attribute information and diagnosisname (diagnosis results) in the case database 152 in association withone another (step S7). Further, the controller 11 sets thenormal/abnormal flag corresponding to this case as “abnormal” (step S8).

After step S6 or step S8, the case data registration processing isfinished.

Note that while in the case data registration processing, the dynamicimage is acquired from the radiographing console 20 at a timing at whichdynamic radiographing is performed at the radiographing apparatus 30(step S1), the acquisition timing of the dynamic image is not limited tothis, and a dynamic image which is radiographed in advance andaccumulated may be acquired.

FIG. 5 is a flowchart illustrating examination mode processing to beexecuted by the medical information management apparatus 10. Theexamination mode processing, which is processing of searching for andreferring to case data accumulated in the case database 152independently from the diagnosis target case, is implemented throughsoftware processing by coordination of the CPU of the controller 11 andthe medical information management program 151 stored in the storage 15.

First, the controller 11 cause an input screen of search conditions tobe displayed at the display 13 and accepts input of the searchconditions through operation by the user from the operator 12 (stepS11). Specifically, “item”, “attribute range” and “normal/abnormal” areinput as the search conditions.

“Item” is an item of motion information to be processed.

“Attribute range” is a search condition relating to attributes fornarrowing down cases to be searched for.

“Normal/abnormal” is a search condition indicating whether only normalcases are to be searched for, only abnormal cases are to be searchedfor, or both normal cases and abnormal cases are to be searched for.

The controller 11 then extracts data which matches the search conditionsfrom the case database 152 (step S12).

For example, in a case where only the normal cases are to be searchedfor, the controller 11 extracts case data for which the “normal/abnormalflag” is “normal” and the “attribute information” is included in theattribute range input in step S11 from the case database 152.

In a case where normal cases and abnormal cases are to be searched for,the controller 11 extracts case data for which the “attributeinformation” is included in the attribute range input in step S11 fromthe case database 152 regardless of the “normal/abnormal flag”.

The controller 11 then creates a graph for the processing target item onthe basis of the extracted case data (step S13). For example, thecontroller 11 creates histogram which indicates a plurality of classesobtained by segmenting numerical values of the processing target iteminto a plurality of classes on a horizontal axis and indicates thenumber of cases (frequency) on a vertical axis. In a case where normalcases and abnormal cases are to be searched for, the controller 11creates a graph so that the normal cases are distinguished from theabnormal cases by changing color, or the like.

The controller 11 then causes search results to be displayed at thedisplay 13 (step S14).

The examination mode processing is finished as described above.

FIG. 6 illustrates an example of a search result screen 131 to bedisplayed at the display 13.

The search result screen 131 includes a search condition display region131A and a search result display region 131B.

In the search condition display region 131A, a mode field 60, an itemfield 61 and an option field 62 are provided.

In the mode field 60, a mode selected by the user is displayed. In thesearch condition display region 131A, “examination mode” is displayed inthe mode field 60.

In the item field 61, an item input by the user as the search conditionsis displayed. In the search condition display region 131A, “maximumspeed of diaphragm” is displayed in the item field 61.

In the option field 62, search conditions (option conditions) other thanthe item input by the user are displayed. In the search conditiondisplay region 131A, “40s, 50s”, “normal” are displayed in the optionfield 62. Here, normal cases for which age is 40s or 50s are to besearched for.

The search result display region 131B includes a total data number field70, an average age field 71, an average value field 72, a median valuefield 73, a graph display field 80, or the like.

In the total data number field 70, a total number of pieces of data ofcases which match the search conditions is displayed.

In the average age field 71, an average age of the cases which match thesearch conditions is displayed. Numerical values in brackets of theaverage age field 71 are a minimum value and a maximum value of age inthe cases which match the search conditions.

In the average value field 72 and the median value field 73, an averagevalue and a median value of the processing target item (maximum speed ofthe diaphragm) in the cases which match the search conditions arerespectively displayed. Numerical values in brackets of the averagevalue field 72 and the median value field 73 are minimum values andmaximum values in the processing target item (maximum speed of thediaphragm) in the cases which match the search conditions.

In the graph display field 80, histogram of the processing target item(maximum speed of the diaphragm) in the cases which match the searchconditions is displayed. In the search result display region 131B,distribution of the “maximum speed of diaphragm” in normal cases forwhich age is 40s or 50s is displayed in the graph display field 80.

In the search result display region 131B, further, an abnormal caseadditional instructor 74 and an item condition change instructor 75 areprovided. The abnormal case additional instructor 74 and the itemcondition change instructor 75 are links to related information.

If the abnormal case additional instructor 74 is depressed throughoperation by the user from the operator 12, search results includingabnormal cases are displayed in the search result display region 131B.

If the item condition change instructor 75 is depressed throughoperation by the user from the operator 12, search results for which theprocessing target item is changed to the “change rate of area ofdiaphragm” are displayed in the search result display region 131B.

FIG. 7 is a flowchart illustrating comparison mode processing to beexecuted by the medical information management apparatus 10. Thecomparison mode processing, which is processing of determining normalityor abnormality of the diagnosis target case by comparing the diagnosistarget case with the case data accumulated in the case database 152, isimplemented through software processing by coordination of the CPU ofthe controller 11 and the medical information management program 151stored in the storage 15.

First, the controller 11 causes an input screen of the search conditionsto be displayed at the display 13 and accepts input of the searchconditions through operation by the user from the operator 12 (stepS21). Specifically, “item”, “attribute range” and “normal/abnormal” areinput as the search conditions.

The search conditions are similar to those in step S11 in theexamination mode processing (see FIG. 5).

Note that it is only necessary to designate a range including attributesof the diagnosis target case as “attribute range”.

The controller 11 then causes a designation screen of the dynamicinformation relating to the diagnosis target case to be displayed at thedisplay 13 and accepts designation of the diagnosis target dynamicinformation (the dynamic image or the motion information) throughoperation by the user from the operator 12 (step S22). The user maydesignate the dynamic image itself as the diagnosis target dynamicinformation or may designate the motion information (such as a numericalvalue) corresponding to the processing target item (item input in thesearch conditions). The diagnosis target dynamic information may beprepared in advance at the storage 15 of the medical informationmanagement apparatus 10 or an external apparatus, or the dynamic imageradiographed at the radiographing apparatus 30 may be acquired from theradiographing console 20.

Here, the controller 11 determines whether or not the designateddiagnosis target dynamic information is the dynamic image (step S23).

In a case where the designated diagnosis target dynamic information isthe dynamic image (step S23: YES), the controller 11 analyzes thedynamic image which is a diagnosis target and calculates a valuecorresponding to the processing target item (step S24).

After step S24 or in step S23, in a case where the designated diagnosistarget dynamic information is not the dynamic image (step S23: NO), thatis, in a case where the motion information corresponding to theprocessing target item is designated as the diagnosis target dynamicinformation, the controller 11 extracts data which matches the searchconditions from the case database 152 (step S25).

For example, in a case where only normal cases are to be searched for,the controller 11 extracts case data for which “normal/abnormal flag” is“normal” and “attribute information” is included in the attribute rangeinput in step S21 from the case database 152.

In a case where normal cases and abnormal cases are to be searched for,the controller 11 extracts case data for which “attribute information”is included in the attribute range input in step S21 from the casedatabase 152 regardless of “normal/abnormal flag”.

The controller 11 then determines normality or abnormality of thediagnosis target dynamic information (step S26).

For example, in a case where only normal cases are to be searched for,the controller 11 determines normality or abnormality of the diagnosistarget dynamic information on the basis of normal case data extractedfrom the case database 152.

In a case where normal cases and abnormal cases are to be searched for,the controller 11 determines normality or abnormality of the diagnosistarget dynamic information on the basis of normal case data and abnormalcase data extracted from the case database 152.

Determination of normality or abnormality of the diagnosis targetdynamic information may be determination of normal (not including adisease) or abnormal (including a disease) or may be determination of adegree (such as a probability and a possibility) of normality and adegree (such as a probability and a possibility) of abnormality.Further, information from which a position of the diagnosis targetdynamic information in distribution of normal cases and/or abnormalcases can be recognized may be generated as well as the degree ofnormality or abnormality being obtained as a numerical value or a level.Further, the controller 11 may specify diagnosis name in a case where itis determined as abnormal (including a disease).

The controller 11 then creates a graph for the processing target item onthe basis of the extracted case data (step S27). For example, thecontroller 11 creates histogram which indicates a plurality of classesobtained by segmenting numerical values of the processing target item ona horizontal axis and indicates the number of cases (frequency) on avertical axis. In a case where normal cases and abnormal cases are to besearched for, the controller 11 creates a graph so that the normal casesare distinguished from the abnormal cases by changing color, or thelike.

The controller 11 then adds a position of the diagnosis target dynamicinformation on the graph (step S28). For example, the controller 11 addsa mark, or the like, to a position from which a class to which thediagnosis target dynamic information belongs on the graph can berecognized.

The controller 11 then causes analysis results to be displayed at thedisplay 13 (step S29). For example, the controller 11 causes statisticaldata of the normal dynamic information (in a case where normal cases areto be searched for), statistical data of the abnormal dynamicinformation (in a case where abnormal cases are to be searched for) andthe diagnosis target dynamic information to be displayed on the samescreen of the display 13.

The comparison mode processing is finished as described above.

FIG. 8 illustrates an example of an analysis result screen 132 to bedisplayed at the display 13. The analysis result screen 132 is anexample in a case where the motion information (value of maximum speedof the diaphragm) is designated as the diagnosis target dynamicinformation.

The analysis result screen 132 includes a search condition displayregion 132A and an analysis result display region 132B.

In the search condition display region 132A, a mode field 60, an itemfield 61, an option field 62 and a diagnosis target value field 63 areprovided.

The mode field 60, the item field 61 and the option field 62 are similarto those on the search result screen 131 (see FIG. 6).

In the search condition display region 132A, “comparison mode” isdisplayed in the mode field 60.

In the search condition display region 132A, “maximum speed ofdiaphragm” is displayed in the item field 61.

In the search condition display region 132A, “40s, 50s” and“normal/abnormal” are displayed in the option field 62. Here, normalcases and abnormal cases for which age is 40s or 50s are to be searchedfor.

In the diagnosis target value field 63, motion information (value of theprocessing target item) designated by the user is displayed as thediagnosis target dynamic information. In the search condition displayregion 132A, in the diagnosis target value field 63, a value of “42.1”is displayed as the maximum speed of the diaphragm.

The analysis result display region 132B includes a total data numberfield 70, an average age field 71, an average value field 72, a medianvalue field 73, a normality/abnormality determination result field 76, adiagnosis target value field 77, a graph display field 80, and the like.

The total data number field 70, the average age field 71, the averagevalue field 72 and the median value field 73 are similar to those on thesearch result screen 131 (see FIG. 6).

In the normality/abnormality determination result field 76, normality orabnormality determined for the diagnosis target value (value of theprocessing target item designated as the diagnosis target dynamicinformation) is displayed. Here, in the normality/abnormalitydetermination result field 76, a probability of “86.1%” which indicatesthat the dynamic information is normal is displayed as a degree ofnormality. For example, a ratio (%) of the number of normal cases to thetotal number of cases (the number of normal cases+the number of abnormalcases) included in the same class as the class to which the diagnosistarget value belongs is set as a probability of normality.

In the diagnosis target value field 77, the motion information (value ofthe processing target item) designated by the user is displayed as thediagnosis target dynamic information in a similar manner to thediagnosis target value field 63.

In the graph display field 80, histogram of the processing target itemof cases which match the search conditions is displayed. In the analysisresult display region 132B, distribution of “maximum speed of diaphragm”of normal cases for which age is 40s or 50s is displayed separately fromdistribution of “maximum speed of diaphragm” of abnormal cases for whichage is 40s or 50s in the graph display field 80.

Further, in the graph display field 80, to which class on the histogram,“42.1” designated as the diagnosis target value belongs is indicatedwith a star mark 81.

In the analysis result screen 132, the statistical data of the normaldynamic information, the statistical data of the abnormal dynamicinformation and the diagnosis target dynamic information are output onthe same screen.

On the analysis result screen 132, the statistical data of the normaldynamic information corresponds to histogram of normal cases in thegraph display field 80. The total number of pieces of normal case dataamong case data which matches the search conditions, and an averagevalue and a median value for the processing target item calculated onlyfrom the normal case data may be output (displayed) as the statisticaldata of the normal dynamic information.

On the analysis result screen 132, the statistical data of the abnormaldynamic information corresponds to histogram of abnormal cases in thegraph display field 80. The total number of pieces of abnormal case dataamong case data which matches the search conditions, and an averagevalue and a median value for the processing target item calculated onlyfrom the abnormal case data may be output (displayed) as the statisticaldata of the abnormal dynamic information.

On the analysis result screen 132, the diagnosis target dynamicinformation corresponds to the value of “42.1” in the diagnosis targetvalue field 77 and the star mark 81 in the graph display field 80.

FIG. 9 illustrates an example of the analysis result screen 133 to bedisplayed at the display 13. The analysis result screen 133 is anexample of a case where the dynamic image is designated as the diagnosistarget dynamic information.

The analysis result screen 133 has substantially the same configurationas the configuration of the analysis result screen 132 (see FIG. 8), andthus, description will be omitted for the same components while the samereference numerals are assigned to components which are similar tocomponents of the analysis result screen 132, and only portionsdifferent from the analysis result screen 132 will be described.

The analysis result screen 133 includes a search condition displayregion 133A and an analysis result display region 133B.

In the search condition display region 133A, a diagnosis target imageinput/no input field 64 and a diagnosis target image field 65 areprovided in place of the diagnosis target value field 63 in the searchcondition display region 132A (see FIG. 8).

In the diagnosis target image input/no input field 64, whether or notthe dynamic image is input as the diagnosis target dynamic informationis displayed.

In a case where “input” is displayed in the diagnosis target imageinput/no input field 64, the dynamic image designated by the user isdisplayed in the diagnosis target image field 65.

In the analysis result display region 133B, a calculation value field 78is provided in place of the diagnosis target value field 77 in theanalysis result display region 132B (see FIG. 8).

In the calculation value field 78, a value of “42.1” of the processingtarget item calculated from the input image is displayed. Comparativesearch can be performed by an image being input even in a case where avalue of the processing target item is not calculated for the diagnosistarget.

In the normality/abnormality determination result field 76, normality orabnormality determined for the value of the processing target itemcalculated from the input image is displayed.

Further, in the graph display field 80, to which class on the histogram,the value of the processing target item calculated from the input imagebelongs is indicated with the star mark 81.

Also on the analysis result screen 133, the statistical data of thenormal dynamic information (histogram of normal cases in the graphdisplay field 80), the statistical data of the abnormal dynamicinformation (histogram of abnormal cases in the graph display field 80)and the diagnosis target dynamic information (the value in thecalculation value field 78, the star mark 81 within the graph displayfield 80, the dynamic image in the diagnosis target image field 65) areoutput on the same screen.

As describe above, according to the first embodiment, by managing thedynamic information (normal dynamic information) obtained by performingdynamic radiographing on a subject who does not have a disease andattribute information (normal attribute information) of the subject whodoes not have a disease in association with each other at the medicalinformation management apparatus 10, the dynamic information obtained byperforming dynamic radiographing on the subject who does not have adisease can be utilized later. This can contribute to high-accuracydiagnosis support and clinical practice. Further, this can also helpeducation of health personnel and studies.

Further, the attribute information (the normal attribute information,the abnormal attribute information) does not include information whichspecifies an individual, so that it is possible to prevent leakage ofpersonal information of the subject (patient) who is a target of dynamicradiographing.

Further, the normal dynamic information is accumulated, so that it ispossible to determine normality or abnormality of second dynamicinformation (diagnosis target dynamic information) obtained byperforming dynamic radiographing on a second subject (diagnosis targetpatient) on the basis of the normal dynamic information.

Further, the normal dynamic information and the normal attributeinformation are accumulated in association with each other, so that itis possible to determine normality or abnormality of the diagnosistarget dynamic information on the basis of the normal dynamicinformation and the normal attribute information.

Further, in addition to the normal dynamic information and the normalattribute information, by managing third dynamic information (abnormaldynamic information) obtained by performing dynamic radiographing on athird subject who has a disease and attribute information (abnormalattribute information) of the third subject who has a disease inassociation with each other at the medical information managementapparatus 10, the normal dynamic information and the abnormal dynamicinformation can be utilized in diagnosis, or the like, in the future.

Further, the normal dynamic information and the abnormal dynamicinformation are accumulated, so that it is possible to determinenormality or abnormality of the diagnosis target dynamic informationobtained by performing dynamic radiographing on the diagnosis targetpatient on the basis of the normal dynamic information and the abnormaldynamic information.

Further, the normal dynamic information and the normal attributeinformation, and the abnormal dynamic information and the abnormalattribute information are respectively accumulated in association witheach other, so that it is possible to determine normality or abnormalityof the diagnosis target dynamic information on the basis of the normaldynamic information, the normal attribute information, the abnormaldynamic information and the abnormal attribute information.

Further, by the user designating the processing target item (motioninformation) and the attribute, it is possible to extract onlycorresponding information from the case database 152 and statisticallydisplay the corresponding information.

For example, as indicated in the analysis result screen 132 in FIG. 8and the analysis result screen 133 in FIG. 9, the statistical data ofthe normal dynamic information, the statistical data of the abnormaldynamic information and the diagnosis target dynamic information can beoutput. By displaying normal cases and abnormal cases so that the normalcases are distinguished from the abnormal cases or indicating a normalrange and/or an abnormal range on a graph, it is possible to provide adifference in a dynamic state between a normal subject and an abnormalsubject.

Note that in a case where attribute information is not used to determinenormality or abnormality of the diagnosis target dynamic information, itis only necessary to eliminate limitation by the attribute information(input of the attribute range in step S21) and set case data of allattributes within the case database 152 as a comparison target in thecomparison mode processing (see FIG. 7).

Further, the extracted statistical data can be output outside. A dataformat for output includes, for example, csv and pdf formats.

Second Embodiment

A second embodiment to which the present invention is applied will bedescribed next.

A medical information management system in the second embodiment has aconfiguration similar to the configuration of the medical informationmanagement system 100 described in the first embodiment, and thus,illustration and description of the configuration will be omitted.Characteristic configuration and processing of the second embodimentwill be described below.

The medical information management apparatus 10 determines normality orabnormality of second dynamic information (diagnosis target dynamicinformation) obtained by performing dynamic radiographing on a secondsubject (diagnosis target patient) by utilizing artificial intelligence(AI).

The controller 11 of the medical information management apparatus 10determines normality or abnormality of the diagnosis target dynamicinformation on the basis of the dynamic information (normal dynamicinformation) obtained by performing dynamic radiographing on a subjectwho does not have a disease, attribute information (normal attributeinformation) of the subject who does not have a disease, third dynamicinformation (abnormal dynamic information) obtained by performingdynamic radiographing on a third subject who has a disease, andattribute information (abnormal attribute information) of the thirdsubject who has a disease.

Specifically, the controller 11 performs machine learning fordetermining normality or abnormality of the dynamic information on thebasis of the case data accumulated in the case database 152 (see FIG. 3)and determines normality or abnormality of the diagnosis target dynamicinformation on the basis of learning results. Machine learning andutilization of learning results are implemented through softwareprocessing by coordination of the CPU of the controller 11 and themedical information management program 151 stored in the storage 15.

At the medical information management apparatus 10, the dynamicinformation, the attribute information and the diagnosis results areassociated with one another in the case database 152 (see FIG. 3), andthus, it can be said that patient parameters including the attributeinformation and the dynamic information are associated with thediagnosis results as illustrated in FIG. 10. The controller 11 generatesa discriminator for determining normality or abnormality of thediagnosis target dynamic information by utilizing this correspondencerelationship to perform machine learning by receiving the patientparameters as input and outputting the diagnosis results. The controller11 determines normality or abnormality of the diagnosis target dynamicinformation using the discriminator which has learned.

As machine learning, support vector machine (SVM), random forest, deeplearning, or the like, can be used.

FIG. 11 is a conceptual diagram illustrating machine learning processingusing the patient parameters (input data) and the diagnosis results(correct data). The controller 11 generates a discriminator by receivinginput of patient parameters such as age (attribute information), sex(attribute information), smoking history (attribute information), bodyheight (attribute information), body weight (attribute information), BMI(attribute information), a pulmonary function test result (attributeinformation), a change rate of an area of a lung field (dynamicinformation), a restenosis rate of a diameter of an airway (dynamicinformation) and speed of a diaphragm (dynamic information) accumulatedin the case database 152 and outputting diagnosis results such as nodisease (normal), COPD, bronchial asthma, lung cancer and diabetes.

FIG. 12 is a conceptual diagram illustrating inference processing ofpredicting diagnosis prediction results (prediction data) from thepatient parameters (input data) which are diagnosis targets using thediscriminator which has learned. The controller 11 inputs attributeinformation (such as age, sex, smoking history, body height, bodyweight, BMI and a pulmonary function test result) and dynamicinformation (such as a change rate of an area of a lung field, arestenosis rate of a diameter of an airway and speed of a diaphragm) ofthe diagnosis target patient to the discriminator which has learned andobtains an output result (diagnosis prediction result). The controller11 outputs no disease (normal) or diagnosis name (such as COPD,bronchial asthma, lung cancer and diabetes) as the diagnosis predictionresult. The controller 11 causes the diagnosis prediction result to bedisplayed at the display 13.

As described above, the second embodiment enables later use of thedynamic information (normal dynamic information) which is managed at themedical information management apparatus 10 and which is obtained byperforming dynamic radiographing on a subject who does not have adisease.

Specifically, it is possible to generate a discriminator for determiningnormality or abnormality of the diagnosis target dynamic information byperforming machine learning by utilizing correspondence relationshipbetween the patient parameters constituted with the attributeinformation and the dynamic information accumulated in the case database152 (see FIG. 3) and the diagnosis results. Use of this discriminatorwhich has learned enables prediction of a diagnosis result of anarbitrary patient. It is therefore possible to prevent a doctor fromoverlooking a disease and support consideration of necessity of othertests.

Note that in the second embodiment, the attribute information does nothave to be used as the patient parameters to generate a discriminatorthrough machine learning. In this case, the controller 11 generates adiscriminator by performing machine learning while receiving the dynamicinformation as input and outputting diagnosis results. The controller 11determines normality or abnormality of the diagnosis target dynamicinformation by receiving input of the diagnosis target dynamicinformation using the discriminator which has learned.

Third Embodiment

A third embodiment to which the present invention is applied will bedescribed next.

A medical information management system in the third embodiment has aconfiguration similar to the configuration of the medical informationmanagement system 100 described in the first embodiment, and thus,illustration and description of the configuration will be omitted.Characteristic configuration and processing of the third embodiment willbe described below.

Also in the third embodiment, the medical information managementapparatus 10 determines normality or abnormality of second dynamicinformation (diagnosis target dynamic information) obtained byperforming dynamic radiographing on a second subject (diagnosis targetpatient) by utilizing AI.

The controller 11 of the medical information management apparatus 10constructs a normal model through machine learning (such as supportvector machine, random forest and deep learning) on the basis of onlythe dynamic information (normal dynamic information) obtained byperforming dynamic radiographing on a subject who does not have adisease and attribute information of the subject who does not have adisease. Construction of a normal model through machine learning andutilization of the normal model are implemented through softwareprocessing by coordination of the CPU of the controller 11 and themedical information management program 151 stored in the storage 15.

As the patient parameters to be used in machine learning, a large volumeof data sets of attribute information (such as age, sex, smokinghistory, body height, body weight, BMI and a pulmonary function testresult) relating to normal cases (subject who does not have a disease)and dynamic information (such as a change rate of an area of a lungfield, a restenosis rate of a diameter of an airway and speed of adiaphragm) is collected.

In the third embodiment, a normal model is created by utilizing pastnormal case data, and normality or abnormality of the diagnosis targetdynamic information is determined by calculating a degree of deviationfrom the normal model, and thus, it is assumed that only normal casedata is accumulated in the case database 152. In other words, all casedata accumulated in the case database 152 is data which does not have adisease (normal), and thus, diagnosis results (normal/abnormal flag,diagnosis name) are not required in the case database 152.

In a case where attention is focused only on normal patients, it isconsidered that respective parameters included in the patient parametersare complicatedly related to each other, and the followingcharacteristics can be extracted.

-   -   Parameters which are consistent and do not change    -   Parameters which are inconsistent and for which values change    -   Parameters which have (positive/negative) correlation with other        parameters

FIG. 13 illustrates processing of creating a normal model throughmachine learning utilizing the patient parameters (the normal attributeinformation, the normal dynamic information) of normal case data.

The controller 11 of the medical information management apparatus 10creates a “normal model” by extracting characteristics of the patientparameters in normal cases by utilizing machine learning. The controller11 automatically derives normal characteristic items of “normal model”.For example, the controller 11 finds an item indicating relationshipbetween body height and BMI as a normal characteristic item 1, an itemindicating relationship between smoking history and a pulmonary functiontest result as a normal characteristic item 2, and an item indicatingrelationship between a restenosis rate of a diameter of an airway and apulmonary function test result as a normal characteristic item 3.

The controller 11 determines normality and abnormality of the diagnosistarget dynamic information by calculating a degree of deviation from“normal model” for the patient parameters (the attribute information andthe dynamic information) relating to the diagnosis target patient.

For example, the controller 11 calculates a degree of deviation from“normal model” of the patient parameters relating to the diagnosistarget patient for each of the normal characteristic items 1, 2, 3, . .. . Then, in a case where at least one of degrees of deviationcorresponding to the respective normal characteristic items is greaterthan a predetermined threshold, the controller 11 may determineabnormality or may comprehensively determine the respective normalcharacteristic items to calculate a comprehensive degree of deviationagain, and in a case where the comprehensive degree of deviation isgreater than a predetermined threshold, the controller 11 may determineabnormality. A method for determining whether or not the dynamicinformation is normal or whether or not the dynamic information isabnormal is not particularly limited.

Further, the controller 11 causes a determination result indicatingwhether or not the dynamic information is normal, a degree of deviationfrom “normal model”, or the like, for the patient parameters relating tothe diagnosis target patient to be displayed at the display 13.

As described above, according to the third embodiment, by calculating adegree of deviation from the normal model created by utilizing thedynamic information (normal dynamic information) which is managed at themedical information management apparatus 10 and which is obtained byperforming dynamic radiographing on a subject who does not have adisease, it is possible to determine normality or abnormality of thediagnosis target dynamic information.

Note that it is also possible to cause AI to collectively analyze andlearn correlation among various normal characteristic items on the basisof the normal case data and directly calculate a degree of deviation(one value) from the normal model as output for the patient parametersof the diagnosis target.

Further, in the third embodiment, the attribute information does nothave to be used as the patient parameters to generate the normal modelthrough machine learning. In this case, the controller 11 creates anormal model by performing machine learning using only the dynamicinformation of normal case data as the patient parameters. Thecontroller 11 determines normality or abnormality of the diagnosistarget dynamic information on the basis of a degree of deviation fromthe normal model of the diagnosis target dynamic information.

Fourth Embodiment

A fourth embodiment to which the present invention is applied will bedescribed next.

A medical information management system in the fourth embodiment has aconfiguration similar to the configuration of the medical informationmanagement system 100 described in the first embodiment, and thus,illustration and description of the configuration will be omitted.Characteristic configuration and processing of the fourth embodimentwill be described below.

The medical information management apparatus 10 presents criteria fordetermining abnormality of second dynamic information (diagnosis targetdynamic information) obtained by performing dynamic radiographing on asecond subject (diagnosis target patient) by utilizing AI.

In the fourth embodiment, the controller 11 of the medical informationmanagement apparatus 10 learns an abnormality determination methodincluding the criteria for determining whether or not the dynamicinformation is abnormal through machine learning (such as support vectormachine, random forest and deep learning). The fourth embodiment is anexample of a way of using the case database 152, which is a way thatcriteria for determining abnormality are proposed by clearly specifyingcriteria for determining abnormality (on which point attention should befocused) obtained through machine learning based on the case data to theuser. Learning of the abnormality determination method, presentation ofthe criteria for determining abnormality and utilization of theabnormality determination method are implemented through softwareprocessing by coordination of the CPU of the controller 11 and themedical information management program 151 stored in the storage 15.

Data sets of attribute information (such as age, sex, smoking history,body height, body weight, BMI and a pulmonary function test result) anddynamic information (such as a change rate of an area of a lung field, arestenosis rate of a diameter of an airway and speed of a diaphragm)relating to respective cases are collected as the patient parameters tobe used in machine learning and accumulated in the case database 152.

FIG. 14 illustrates processing of extracting characteristics on whichimportance is placed in determination as to whether or not the dynamicinformation is abnormal, through machine learning utilizing the patientparameters of the case data.

The input data may be either only normal case data, only abnormal casedata or mixture of normal case data and abnormal case data.

In a case where only normal case data is used as the input data, thecontroller 11 extracts characteristics (abnormality determinationcharacteristic items) on which importance is placed in determination ofabnormality, from characteristics of respective parameters in normalcases through machine learning utilizing the patient parameters of thenormal case data.

In a case where only abnormal case data is used as the input data, thecontroller 11 extracts characteristics on which importance is placed indetermination of abnormality, from characteristics of respectiveparameters in abnormal cases through machine learning utilizing thepatient parameters of the abnormal case data.

In a case where normal case data and abnormal case data are used as theinput data, the controller 11 also receives input of normal/abnormallabels (normal/abnormal flags) for the respective pieces of case dataand extracts characteristics on which importance is placed indetermination of abnormality from characteristics of respectiveparameters in normal cases and characteristics of respective parametersin abnormal cases through machine learning utilizing the patientparameters of the normal case data and the abnormal case data.

The controller 11 automatically derives abnormality determinationcharacteristic items by utilizing machine learning. For example, thecontroller 11 finds an item indicating relationship between body heightand BMI as an abnormality determination characteristic item 1, an itemindicating relationship between smoking history and a pulmonary functiontest result as an abnormality determination characteristic item 2, andan item indicating a restenosis rate of a diameter of an airway alone asan abnormality determination characteristic item 3.

The controller 11 causes the abnormality determination characteristicitems 1, 2, 3, . . . obtained through machine learning to be displayedat the display 13 to present criteria for determining abnormality to theuser.

As described above, according to the fourth embodiment, by analyzingcase data managed at the medical information management apparatus 10, itis possible to propose abnormality determination characteristic itemswhich become criteria for determining abnormality to the user.

Note that in the fourth embodiment, the attribute information does nothave to be used as the patient parameters in extraction of the criteriafor determining abnormality and in machine learning of the abnormalitydetermination method. In this case, the controller 11 performs machinelearning using only the dynamic information of case data as the patientparameters to learn the abnormality determination method including thecriteria.

Description in the above-described embodiments is an example of themedical information management apparatus, the data structure of medicalinformation and the medical information management program according tothe present invention, and the present invention is not limited to this.Detailed configurations and detailed operation of respective componentscan be also changed as appropriate within a scope not deviating from thegist of the present invention.

For example, characteristic configurations and processing of therespective embodiments may be combined.

Further, the data structure of the case database 152 is not limited tothe illustrated example and can be changed depending on the intendeduse. The case database 152 may be managed for each patient or may bemanaged for each disease (including a case of no disease).

Further, except the third embodiment, while both normal cases andabnormal cases are accumulated in the case database 152 and normality isdistinguished from abnormality with the normal/abnormal flag, case datamay be separately accumulated in a normal case database and in anabnormal case database. In this case, the normal dynamic information andthe normal attribute information are managed in association with eachother in the normal case database, and the abnormal dynamic informationand the abnormal attribute information are managed in association witheach other in the abnormal case database. Further, diagnosis name may beassociated with each piece of case data in the abnormal case database.

Further, the case database 152 may include only the normal/abnormal flagas a diagnosis result for each piece of case data without includingdiagnosis name. In this case, it is possible to utilize whether or notthe subject has a disease (normal or abnormal) for each piece of casedata.

Further, each piece of case data in the case database 152 does not haveto include information (normal/abnormal flag) which directly indicatesnormality/abnormality, and whether or not the subject has a disease(normal or abnormal) may be able to be determined from other informationsuch as diagnosis name. Specifically, the case database 152 includesonly diagnosis name as a diagnosis result for each piece of case datawithout including the normal/abnormal flag. In this case, it is possibleto determine that the subject does not have a disease by making thediagnosis name blank or setting “no disease” for normal cases.

Further, a program for executing each kind of processing at eachapparatus may be stored in a portable recording medium. Still further, acarrier wave may be applied as a medium which provides data of a programvia a communication line.

Although embodiments of the present invention have been described andillustrated in detail, the disclosed embodiments are made for purposesof illustration and example only and not limitation. The scope of thepresent invention should be interpreted by terms of the appended claims.

What is claimed is:
 1. A medical information management apparatuscomprising a hardware processor that manages first dynamic informationobtained by performing dynamic radiographing on a first subject thatdoes not have a disease and first attribute information of the firstsubject, the first dynamic information and the first attributioninformation being associated with each other.
 2. The medical informationmanagement apparatus according to claim 1, wherein the first subjectdoes not have the disease of a specific part.
 3. The medical informationmanagement apparatus according to claim 2, wherein the specific part isat least one of parts relating to respiration, circulation, orthopedics,and swallowing.
 4. The medical information management apparatusaccording to claim 1, wherein the first dynamic information includesmotion information obtained by analyzing a dynamic image.
 5. The medicalinformation management apparatus according to claim 4, wherein themotion information is related to motion of a specific part of the firstsubject.
 6. The medical information management apparatus according toclaim 1, wherein the first attribute information does not includeinformation that specifies an individual.
 7. The medical informationmanagement apparatus according to claim 1, wherein the hardwareprocessor determines whether second dynamic information obtained byperforming dynamic radiographing on a second subject is normal orabnormal, based on the first dynamic information.
 8. The medicalinformation management apparatus according to claim 7, wherein thehardware processor determines whether the second dynamic information isnormal or abnormal, based on the first dynamic information and the firstattribute information.
 9. The medical information management apparatusaccording to claim 1, wherein the hardware processor manages thirddynamic information obtained by performing dynamic radiographing on athird subject that has a disease and third attribute information of thethird subject, the third dynamic information and the third attributioninformation being associated with each other.
 10. The medicalinformation management apparatus according to claim 9, wherein the thirdattribute information does not include information that specifies anindividual.
 11. The medical information management apparatus accordingto claim 9, wherein the hardware processor outputs statistical data ofthe first dynamic information, statistical data of the third dynamicinformation, and second dynamic information obtained by performingdynamic radiographing on a second subject.
 12. The medical informationmanagement apparatus according to claim 11, wherein the hardwareprocessor outputs the statistical data of the first dynamic information,the statistical data of the third dynamic information, and the seconddynamic information on an identical screen.
 13. The medical informationmanagement apparatus according to claim 9, wherein the hardwareprocessor determines whether second dynamic information obtained byperforming dynamic radiographing on a second subject is normal orabnormal, based on the first dynamic information and the third dynamicinformation.
 14. The medical information management apparatus accordingto claim 13, wherein the hardware processor determines whether thesecond dynamic information is normal or abnormal, based on the firstdynamic information, the first attribute information, the third dynamicinformation, and the third attribute information.
 15. The medicalinformation management apparatus according to claim 7, wherein thehardware processor determines whether the second dynamic information isnormal or abnormal, based on a statistical analysis.
 16. The medicalinformation management apparatus according to claim 7, wherein thehardware processor determines whether the second dynamic information isnormal or abnormal, based on machine learning.
 17. The medicalinformation management apparatus according to claim 7, wherein thenormality includes a degree of normality of the second dynamicinformation, and the abnormality includes a degree of abnormality of thesecond dynamic information.
 18. A data structure of medical informationto be used by a dynamic information processing apparatus that processesdynamic information obtained by performing dynamic radiographing, thedata structure comprising: dynamic information data obtained byperforming dynamic radiographing on a subject that does not have adisease; and attribute information data of the subject associated withthe dynamic information data.
 19. A non-transitory computer-readablestorage medium storing a medical information management program causinga computer to perform a management process of managing first dynamicinformation obtained by performing dynamic radiographing on a firstsubject that does not have a disease and first attribute information ofthe first subject, the first dynamic information and the firstattribution information being associated with each other.
 20. Thestorage medium according to claim 19, wherein the first subject does nothave the disease of a specific part.
 21. The storage medium according toclaim 20, wherein the specific part is at least one of parts related torespiration, circulation, orthopedics, and swallowing.
 22. The storagemedium according to claim 19, wherein the first dynamic informationincludes motion information obtained by analyzing a dynamic image. 23.The storage medium according to claim 22, wherein the motion informationis related to motion of a specific part of the first subject.
 24. Thestorage medium according to claim 19, wherein the first attributeinformation does not include information that specifies an individual.25. The storage medium according to claim 19, wherein the program causesthe computer to perform a determination process of determining whethersecond dynamic information obtained by performing dynamic radiographingon a second subject is normal of abnormal, based on the first dynamicinformation.
 26. The storage medium according to claim 25, wherein, inthe determination process, the computer determines whether the seconddynamic information is normal or abnormal, based on the first dynamicinformation and the first attribute information.
 27. The storage mediumaccording to claim 19, wherein, in the management process, the computermanages third dynamic information obtained by performing dynamicradiographing on a third subject that has a disease and third attributeinformation of the third subject, the third dynamic information and thethird attribution information being associated with each other.
 28. Thestorage medium according to claim 27, wherein the third attributeinformation does not include information that specifies an individual.29. The storage medium according to claim 27, wherein the program causesthe computer to perform an output process of outputting statistical dataof the first dynamic information, statistical data of the third dynamicinformation, and second dynamic information obtained by performingdynamic radiographing on a second subject.
 30. The storage mediumaccording to claim 29, wherein, in the output process, the statisticaldata of the first dynamic information, the statistical data of the thirddynamic information, and the second dynamic information are output on anidentical screen.
 31. The storage medium according to claim 27, whereinthe program causes the computer to perform a determination process ofdetermining whether second dynamic information obtained by performingdynamic radiographing on a second subject is normal or abnormal, basedon the first dynamic information and the third dynamic information. 32.The storage medium according to claim 31, wherein, in the determinationprocess, the computer determines whether the second dynamic informationis normal or abnormal, based on the first dynamic information, the firstattribute information, the third dynamic information, and the thirdattribute information.
 33. The storage medium according to claim 25,wherein, in the determination process, the computer determines whetherthe second dynamic information is normal or abnormal, based on astatistical analysis.
 34. The storage medium according to claim 25,wherein, in the determination process, the computer determines whetherthe second dynamic information is normal or abnormal, based on machinelearning.
 35. The storage medium according to claim 25, wherein thenormality includes a degree of normality of the second dynamicinformation, and the abnormality includes a degree of abnormality of thesecond dynamic information.